code: def h2o_regression_pipeline_evaluation():
      data = get_synthetic_regression_data()
  
      pipeline = pipeline_h2o_regr()
      train_data, test_data = train_test_data_setup(data)
  
      pipeline.fit(input_data=train_data)
      _ = pipeline.predict(input_data=test_data)
      _, rmse_on_test = get_rmse_value(pipeline, train_data, test_data)
      print(f"RMSE {rmse_on_test}")
  
file_path: FEDOT/examples/advanced/automl/h2o_example.py
imports: import numpy
  from sklearn.metrics import roc_auc_score, mean_squared_error, mean_absolute_error
  from examples.advanced.time_series_forecasting.composing_pipelines import visualise
  from examples.simple.pipeline_import_export import create_correct_path
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.operation_types_repository import OperationTypesRepository
  from test.unit.tasks.test_classification import get_iris_data
  from test.unit.tasks.test_forecasting import get_ts_data
  from test.unit.tasks.test_regression import get_synthetic_regression_data, get_rmse_value
label_desc: Use image node_id 6 for calling a global function with example usage: h2o_regression_pipeline_evaluation() without return types